AI fluency is a learning curve. A workforce does not become AI-fluent when licenses land. It becomes fluent when people learn, role by role, how to use AI inside real work: asking better questions, choosing the right model, iterating with the system, validating the answer, and improving the work product managers actually review.
The website implication from Anthropic's March 2026 Economic Index is simple. Automation is only one part of the labor-market story. Practice is the other. Experienced AI users appear to work differently from newer users, and that difference is measurable enough to belong inside an AI Audit.
Anthropic measured a real learning curve.
Anthropic's Economic Index report, published March 24, 2026, studied Claude usage from February 5 to February 12, 2026 across Claude.ai and first-party API traffic. The report compares lower-tenure users with users who had been on Claude for at least six months.
The high-tenure group used Claude more for work, brought more complex tasks, used it more collaboratively, and had higher conversation success. Anthropic reports that experienced users were more likely to use Claude for work and less likely to use purely directive patterns. Its controlled regressions still found a success-rate lift for higher-tenure users after accounting for task, country, model, language, and use case.
The finding matters because it gives AI Fluency a third-party anchor. The gap between a license and a capability is not a slogan. The same tool produces different outcomes as users learn how to work with it.
Tenure changes the work pattern.
Experienced AI users are not merely using the system more. They are bringing harder work, collaborating more, and producing more successful conversations. That is the shape a fluency program should measure.
For finance, fluency is a control surface.
Finance firms already know that two analysts with access to the same tool can produce radically different work. One analyst uses AI as a search box and pastes the answer into a memo. Another uses it to pressure-test the thesis, reconcile citations, generate alternatives, and validate the final output against source documents. The tool count is identical. The operating risk is not.
This is why workforce AI Fluency belongs next to AI Transformation and AI Governance. A fluent team can turn AI into cycle-time, quality, and decision-speed gains. A less fluent team can create false confidence, unreviewed output, and policy exceptions that look healthy in activity dashboards.
Anthropic also found model selection matching task value: users bring more complex, higher-value work to more capable models. That belongs in the finance operating read. A bank, PE firm, insurer, or asset manager should know whether high-value workflows are routed to stronger models and stricter review paths, while lower-risk tasks stay cheaper and faster.
The scorecard should ask better fluency questions.
Login counts and training completion are weak signals. The stronger questions read the learning curve directly.
How long have the power users in each role been using AI in real work, not demos or training labs?
Are teams iterating, validating, and learning with AI, or delegating one-off tasks and accepting the first answer?
Are high-value workflows routed to the right model class and review path, with cost and risk matched to the work?
Can managers name the top use cases, read the AI-augmented work, and confirm whether output quality improved?
The operating goal is to separate casual use from capability. The same telemetry that helps a manager improve a team also helps a board understand whether AI is compounding on the balance sheet or only appearing in dashboard activity.
What the AI Audit should produce.
A useful AI Audit should leave the workforce side with more than "people are using AI." It should show the learning curve by role: who has access, who uses AI inside priority workflows, who has practiced long enough to use it well, which managers can validate the output, and where training or tooling is still blocking the curve.
For TrustEvals, this strengthens the existing AI Fluency workstream. The Audit reads the current state. AI Fluency moves the workforce through role-specific tooling, workflow training, pattern libraries, telemetry, and manager-readable scoring.
The finance leader's question becomes concrete: where do we have AI power users, where do we only have licenses, and where is the learning curve steep enough to fund the next workflow?
Source
Anthropic Economic Index report: Learning curves, published March 24, 2026. The report studies Claude usage in February 2026 and analyzes model selection, usage tenure, collaboration modes, task complexity, and conversation success.